552 research outputs found

    Separation of Synchronous Sources

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    This thesis studies the Separation of Synchronous Sources (SSS) problem, which deals with the separation of signals resulting from a linear mixing of sources whose phases are synchronous. While this study is made in a form independent of the application, a motivation from a neuroscience perspective is presented. Traditional methods for Blind Source Separation, such as Independent Component Analysis (ICA), cannot address this problem because synchronous sources are highly dependent. We provide sufficient conditions for SSS to be an identifiable problem, and quantify the effect of prewhitening on the difficulty of SSS. We also present two algorithms to solve SSS. Extensive studies on simulated data illustrate that these algorithms yield substantially better results when compared with ICA methods. We conclude that these algorithms can successfully perform SSS in varying configurations (number of sources, number of sensors, level of additive noise, phase lag between sources, among others). Theoretical properties of one of these algorithms are also presented. Future work is discussed extensively, showing that this area of study is far from resolved and still presents interesting challenges

    Clustering evoked potential signals using subspace methods

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    This work proposes a clustering technique to analyze evoked potential signals. The proposed method uses an orthogonal subspace model to enhance the single-trial signals of a session and simultaneously a subspace measure to group the trials into clusters. The ensemble averages of the signals of the different clusters are compared with ensemble averages of visually selected trials which are free of any artifact. Preliminary results consider recordings from an occipital channel where evoked response P100 wave is most pronounced.info:eu-repo/semantics/publishedVersio

    Mathematical frameworks for oscillatory network dynamics in neuroscience

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    The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear—for example, heteroclinic network attractors. In this review we present a set of mathemat- ical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical frame- work for further successful applications of mathematics to understanding network dynamics in neuroscience

    Signal Separation and Tracking Algorithm for Multi-Person Vital Signs by Using Doppler Radar

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    Noninvasive monitoring is an important Internet-of-Things application, which is made possible with the advances in radio-frequency based detection technologies. Existing techniques however rely on the use of antenna array and/or frequency modulated continuous wave radar to detect vital signs of multiple adjacent objects. Antenna size and limited bandwidth greatly limit the applicability. In this paper, we propose our system termed ‘DeepMining’ which is a single-antenna, narrowband Doppler radar system that can simultaneously track the respiration and heartbeat rates of multiple persons with high accuracy. DeepMining uses a number of signal observations over a period of time as input and returns the trajectory of the respiration and heartbeat rates of each person. The extraction is based on frequency separation algorithms using successive signal cancellation. The proposed system is implemented using the self-injection locking radar architecture and tested in a series of experiments, showing accuracies of 90% and 85% for two and three objects, respectively, even for closely located persons

    Automatic Drum Transcription and Source Separation

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    While research has been carried out on automated polyphonic music transcription, to-date the problem of automated polyphonic percussion transcription has not received the same degree of attention. A related problem is that of sound source separation, which attempts to separate a mixture signal into its constituent sources. This thesis focuses on the task of polyphonic percussion transcription and sound source separation of a limited set of drum instruments, namely the drums found in the standard rock/pop drum kit. As there was little previous research on polyphonic percussion transcription a broad review of music information retrieval methods, including previous polyphonic percussion systems, was also carried out to determine if there were any methods which were of potential use in the area of polyphonic drum transcription. Following on from this a review was conducted of general source separation and redundancy reduction techniques, such as Independent Component Analysis and Independent Subspace Analysis, as these techniques have shown potential in separating mixtures of sources. Upon completion of the review it was decided that a combination of the blind separation approach, Independent Subspace Analysis (ISA), with the use of prior knowledge as used in music information retrieval methods, was the best approach to tackling the problem of polyphonic percussion transcription as well as that of sound source separation. A number of new algorithms which combine the use of prior knowledge with the source separation abilities of techniques such as ISA are presented. These include sub-band ISA, Prior Subspace Analysis (PSA), and an automatic modelling and grouping technique which is used in conjunction with PSA to perform polyphonic percussion transcription. These approaches are demonstrated to be effective in the task of polyphonic percussion transcription, and PSA is also demonstrated to be capable of transcribing drums in the presence of pitched instruments
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